Multi-Stage Autonomous Localization Architecture for Charging Electric Vehicles

2. The method of claim 1, wherein the gross localization procedure comprises processing the first image by a first convolutional neural network configured to generate a three-element output vector that represents the target position for the camera in the three-dimensional space relative to a current position of the camera, and wherein the fine localization procedure comprises processing the second image by a second convolutional neural network configured to generate an output vector with at least one position coordinate and at least one orientation coordinate.

3. The method of claim 2, wherein, prior to performing the gross localization procedure, the convolutional neural network is trained based on a set of training data that includes a set of input images and corresponding target output vectors.

4. The method of claim 2, wherein the three-element output vector includes a radial coordinate, an angular coordinate, and an azimuth coordinate.

5. The method of claim 2, wherein the at least one position coordinate includes at least one of a radial coordinate, an angular coordinate, and a height coordinate, and wherein the at least one orientation coordinate includes an angular rotation coordinate associated with a corresponding axis.

6. The method of claim 1, wherein the fine localization procedure comprises processing the second image to apply feature detection and/or feature matching algorithms to locate the object in the second image.

7. The method of claim 1, wherein the gross localization procedure comprises processing the first image by a neural network configured to perform object detection, wherein an output of the neural network comprises at least one of coordinates for a bounding box or a segmentation mask.

8. The method of claim 7, wherein the gross localization procedure further comprises processing the coordinates for the bounding box to calculate the estimated target position.

9. The method of claim 1, wherein the gross localization procedure comprises processing the first image by a first convolutional neural network, and the fine localization procedure comprises processing the second image by a second convolutional neural network.

10. The method of claim 9, wherein the first convolutional neural network includes fewer convolution layers than the second convolutional neural network.

11. The method of claim 1, wherein the gross localization procedure is performed by a processor, and wherein the fine localization procedure is performed by a machine learning (ML) accelerator connected to the processor.

12. The method of claim 11, wherein the ML accelerator is configured to implement a convolutional neural network configured to generate an output vector that includes three position coordinates and at least one orientation coordinate.

14. The system of claim 13, wherein the gross localization procedure comprises processing the first image by a first convolutional neural network configured to generate a three-element output vector that represents the target position for the camera in the three-dimensional space relative to a current position of the camera, and wherein the fine localization procedure comprises processing the second image by a second convolutional neural network configured to generate an output vector with at least one position coordinate and at least one orientation coordinate.

16. The system of claim 13, wherein the fine localization procedure comprises processing the second image to apply feature detection and/or feature matching algorithms to locate the object in the second image.

17. The system of claim 13, wherein the camera assembly is mounted on a plug associated with a charging port of an electric vehicle, and wherein moving the camera assembly comprises generating signals for one or more actuators configured to move the plug in the three-dimensional space.

19. The non-transitory computer-readable storage medium of claim 18, wherein the gross localization procedure comprises processing the first image by a first convolutional neural network configured to generate a three-element output vector that represents the target position for the camera in the three-dimensional space relative to a current position of the camera.

20. The non-transitory computer-readable storage medium of claim 19, wherein the fine localization procedure comprises processing the second image by a second convolutional neural network configured to generate an output vector with at least one position coordinate and at least one orientation coordinate.

21. The non-transitory computer-readable storage medium of claim 18, wherein the fine localization procedure comprises processing the second image to apply feature detection and/or feature matching algorithms to locate the object in the second image.